Apart from evaluating the applicability of the proposed \(L_{IL}\)  loss function with different classification networks, we also compared it with the state-of-the-art incremental learning schemes (based on knowledge distillation [1]}, [2]} and contrastive learning [3]}). The comparison is reported in Table REF . We can observe here that the incremental ResNet101 model trained using the proposed \(L_{IL}\)  loss function outperformed the state-of-the-art schemes for each dataset. Furthermore, we can also appreciate the significance of \(L_{MD}\)  objective function within \(L_{IL}\)  through Table REF , where \(L_{MD}\)  allowed the candidate classification model to understand the mutual information between incrementally learned disease representations that improved its performance by many folds on each dataset (especially on Shenzhen [4]}, JSRT [5]}, and Zhang CXR [6]}).
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